On Theoretical Principle and Practical Applicability of Ranked Nodes Method for Constructing Conditional Probability Tables of Bayesian Networks

Pekka Laitila, Kai Virtanen

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

This paper provides new insight into the theoretical principle and the practical applicability of the ranked nodes method (RNM) that is used to construct conditional probability tables (CPTs) for Bayesian networks (BNs) by expert elicitation. RNM is designed for specific types of discrete random variables called ranked nodes that are common in real-world applications of BNs. Despite its active use in recent years, there remains ambiguity about the exact theoretical basis of RNM which can hamper its effective employment. In addition, there are a lack of studies about the general ability of CPTs generated with RNM to represent probabilistic relationships in real-world applications. In this paper, it is shown how the generation of probabilities with RNM is underpinned by a regression model of continuous random variables. Then, it is experimentally determined that in typical applications of RNM, one can generate in a matter of seconds CPTs whose elements reflect well probabilities given by the underlying regression model. Another experiment discovers that CPTs generated with RNM provide a good average fit to a large portion of various real-world CPTs investigated. This confirms the usefulness of RNM in practical applications. The results of the experiment also indicate that choices made by the user of RNM can considerably impact the ability of a generated CPT to represent a given probabilistic relationship. This paper then provides practical advice on the efficient use of RNM with regard to the user-controlled features explored in the experiment.

Original languageEnglish
Pages (from-to)1943-1955
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume50
Issue number5
Early online date31 Jan 2018
DOIs
Publication statusPublished - May 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian networks (BNs)
  • conditional probability tables (CPTs)
  • probability elicitation
  • ranked nodes

Fingerprint

Dive into the research topics of 'On Theoretical Principle and Practical Applicability of Ranked Nodes Method for Constructing Conditional Probability Tables of Bayesian Networks'. Together they form a unique fingerprint.

Cite this